Enrollment apparatus, system, and method

ABSTRACT

An apparatus for enrolling a package is disclosed including: a receiving surface for receiving the package; at least one weight sensor in communication with the receiving surface which generates a weight signal indicative of the weight of the package; at least one video camera which generates a video signal indicative of an image of the package on the receiving surface; and a processor in communication with the at least one weight sensor and the at least one video camera. The processor includes: a weight module which produces, in response to the weight signal, weight data indicative of the weight of the package; and a dimension capture module which produces, in response to the video signal, dimension data indicative of the size of the package. In some embodiments, the processor further includes a recognition module which produces, in response to the video signal, character data indicative of one or more characters present on the package.

CROSS REFERENCE TO RELATED APPLICATION

This application claims benefit of U.S. Provisional Application Ser. No.60/990,115 filed Nov. 26, 2007 and U.S. Provisional Application Ser. No.61/082,762 filed Jul. 22, 2008, the contents of each of which areincorporated herein by reference in their entirety.

BACKGROUND

In industries such as the postal, courier, and supply chain industries,package objects (e.g. letters or parcels) are enrolled into a system fortracking and/or delivery. For example, items presented at post officesand related locations for onward delivery currently require the counterstaff and/or customers to manually enter data regarding addressinformation, weight, dimensional information, and other shipmentcharacteristics. The manual collection of this information is costly, interms of time, error rates on manual data entry, plus errors incorrectly rating items. Retail locations are also typically required tomaintain space for both the scale and the metering devices.

Some postal authorities mandate the capture of additional informationfor items being accepted across post office counters. It is now a commonrequirement that dimensions, destination, and sender information becaptured. Complexity is increasing while the ability to maintain a welltrained counter staff is declining. Increasing use of franchise pointsof presence is causing compliance, training, accounting, and securityproblems.

For example, a customer may bring a package to a post office point ofsale. A postal employee will receive the package, gather informationrelated to the package (e.g. intended destination, package dimensions,package weight, type of delivery, desired delivery date, customerinformation, payment information etc.). In typical situations, theinformation is gathered manually, and in a highly linear and laborintensive fashion. For example, in a typical transaction, a postalemployee might receive a package, weigh it, measure, its dimensions witha tape measure, enter this information into a computer, read a label onthe package, enter delivery address information found on this label,query the customer about desired delivery type and date, enter thisinformation into a computer, provide pricing information to thecustomer, accept payment, etc.

Improvement in enrollment efficiency could provide substantial savingsin, for example, labor costs, error costs, and time.

SUMMARY

The inventors have realized that enrollment efficiency can be increasedby automatically, and substantially simultaneously collecting multipletypes of information about an item being enrolled in a delivery system.

In one aspect, an enrollment device is disclosed which will replace boththe traditional weigh scale, as well as the postage meter, which arecurrently found at induction points for Postal, Courier and Supply Chainoperations. A combination of Optical Character Recognition (OCR) anddimension capture (e.g. using optical dimension capture and/orultrasonic range-finding technologies) is used to capture and convertaddressing, payment, account and shipment related data, plus weight anddimensional information (when relevant) from packages, letters, anddocumentation which are placed on, in, or near the device.

Such a device provides a “front end” mechanism for entering shipmentrelated data into a business environment (e. g. postal environment) andsimultaneously automates the rating and data collection process foraccepting goods and services, automates the process of capturingdimensional data in the course of rating shipments at point of inductioninto the business environment, reduces or eliminates the requirement fora separate weigh scale, reduces or eliminates the requirement for aseparate metering device, and presents data to the organization'sback-end and enterprise systems at point of induction.

In one aspect, an apparatus for enrolling a package is disclosedincluding: a receiving surface for receiving the package; at least oneweight sensor in communication with the receiving surface whichgenerates a weight signal indicative of the weight of the package; atleast one video camera which generates a video signal indicative of animage of the package on the receiving surface; and a processor incommunication with the at least one weight sensor and the at least onevideo camera. The processor includes: a weight module which produces, inresponse to the weight signal, weight data indicative of the weight ofthe package; and a dimension capture module which produces, in responseto the video signal, dimension data indicative of the size of thepackage. In some embodiments, the processor further includes arecognition module which produces, in response to the video signal,character data indicative of one or more characters present on thepackage.

Some embodiments include a range finder sensor in communication with theprocessor which produces a range finder signal indicative of the size ofthe package, and where the dimension capture module produces, inresponse to the video signal and the range finder signal, dimension dataindicative of the size of the package. In some embodiments, thedimension capture module produces, in response to the video signal,dimension data indicative of the size of the package along two axeslying in a plane substantially parallel to the receiving surface, andproduces, in response to the range finder signal, dimension dataindicative of the size of the package along an axis transverse to theplane.

In some embodiments, the dimension capture module includes a trackingmodule which produces, in response to the video signal, tracking dataindicative of the presence and location of the package on the receivingsurface.

In some embodiments, the at least one camera selectively operates in afirst mode characterized by a relatively large field of view to generatea video signal characterized by a relatively low resolution andrelatively high frame rate, and a second mode characterized by arelatively small field of view to generate a video signal characterizedby a relatively high resolution and a relatively low frame rate. In someembodiments, the tracking module produces, in response to the videosignal generated by the at least one camera in the first mode, trackingdata indicative of the presence and location of the package on thereceiving surface. The recognition module produces, in response to thevideo signal generated by the at least one camera in the second mode,character data indicative of one or more characters present on thepackage.

In some embodiments, the dimension capture module includes an edgefinder module which produces, in response to the video signal, edge dataindicative of the location of one or more edges on the package.

In some embodiments, the dimension capture module includes one or moreof: a frame differencing module which generates difference images fromtwo or more images of the video signal; and a color masking moduleconfigured to generate information indicative of the presence orlocation of the package based on color information from the videosignal.

In some embodiments, the dimension capture module includes a Houghtransformation module which applies the Hough transformation to one ormore images from the video signal, and analyzes the transformed imagesto determine information indicative of the size or location of edges onthe package. Some embodiments further include a rectangle trackingmodule configured to track the location of a substantially rectangularpackage based on the information indicative of the size or location ofedges on the package.

In some embodiments, the recognition module includes: an imageprocessing module for processing one or more images from the videosignal; and an analysis module which analyzes the one or more processedimages to produce the character data indicative of one or morecharacters present on the package.

In some embodiments, the image processing module includes at least oneof: a color manipulating module for modifying the color of the one ormore images; a linear filter module for applying a linear image filterto the one or more images; and a morphological filter module forapplying one or more morphological operations to the one or more images.

In some embodiments, the image processing module includes an imagesegmentation module for segmenting the one or more images into one ormore regions of interest based on the content of the images.

In some embodiments, the image processing module includes an imagerotation module which rotates at least a portion of the one or moreimages. In some embodiments, the image rotation module is incommunication with the dimension capture module, and rotates at least aportion of the one or more images based on information from thedimension capture module indicative of the location of the package.

In some embodiments, the at least one camera include at least twocameras, each generating a respective video signal, and where thecameras have least partially overlapping fields of view. The processorincludes an image stitching module which, in response to the respectivevideo signals, combines overlapping images from the at least two camerasto produce a single image of the combined field of view of the cameras.

In some embodiments, each of the at least one cameras has a respectivefield of view. Substantially all locations on the receiving surface fallwithin the respective field of view. In some embodiments, the at leastone camera consists of a single camera.

In some embodiments, the receiving surface is at least partiallytransparent, and where the at least one camera is positioned below thereceiving surface to image the package through the receiving surface.

In some embodiments, the at least one camera is positioned above thereceiving surface.

In some embodiments, the receiving surface includes at least on indiciafor aligning or focusing the at least one camera.

In some embodiments, the at least one camera includes an autofocus.

Some embodiments include an integrated housing including the receivingsurface and processor.

Some embodiments include an arm extending between a proximal endconnected to the housing and a distal end positioned above the receivingsurface, the distal end including at least one chosen from the groupconsisting of: a range finder, and the at least one camera.

Some embodiments include an RFID reader, and/or a bar code reader.

In some embodiments, the rangefinder includes at least one of: anultrasonic range finder, a RADAR range finder, a LIDAR range finder, alaser range finder, an LED based range finder, a mechanical rangefinder, and an optical range finder.

In some embodiments, the at least one weight sensor includes: a loadcell, a MEMs device, a piezoelectric device, a spring scale, and abalance scale.

In some embodiments, the data indicative of one or more characterspresent on the package include data indicative of at least one chosenfrom the group consisting of: an alphanumeric character, a symbol, apostal code, a post mark, a bar code, and a two dimensional bar code.

Some embodiments include a postal meter in communication with theprocessor and/or a printer in communication with the processor.

In another aspect, a method of enrolling a package is disclosedincluding: providing an enrollment apparatus of the type describedherein, using the enrollment apparatus to determine informationindicative of the size of the package; using the enrollment apparatus todetermine information indicative of the size of the package indicativeof one or more characters present on the package; and outputting theinformation indicative of the weight, size, and one or more characterspresent on the package.

In some embodiments, the information indicative of the weight, size, andone or more characters present on the package, respectively, aredetermined substantially in parallel.

In another aspect, a system is disclosed including: an enrollmentapparatus of the type described herein and a package management system.The enrollment apparatus is in communication with the package managementsystem to provide information indicative of the weight, size, and one ormore characters present on the package.

In some embodiments, the package management system includes at least oneof: a package delivery system, a supply chain management system, aninventory management system, and a chain of custody management system.

In some embodiments, the package management system includes a point ofsale unit, and where the point of sale unit generates and displays, inresponse to the information indicative of the weight, size, and one ormore characters present on the package, information indicative of one ormore available service options. In some embodiments, the point of saleunit includes an input unit for receiving information from a user; and aservice unit for providing information indicative of one or moreavailable service options based on the information from the user andinformation indicative of the weight, size, and one or more characterspresent on the package. In some embodiments, where the input unitincludes the enrollment apparatus.

In some embodiments, the input unit receives a series of instructionsfrom the user, and the service unit includes a backwards chaining logicunit which dynamically determines and displays available service optionsbased on the series of instructions and based information indicative ofthe weight, size, and one or more characters present on the package.

In some embodiments, the package management system includes a point ofservice unit, and further including a handler module for facilitatingcommunication between the enrollment apparatus and the point of serviceunit.

As used herein, the “location” of a package refers to its position inspace and its orientation.

Various embodiments may include any of the features described above,either alone or in any combination.

The details of one or more embodiments are set forth in the accompanyingdrawings and the description below. Other features and advantages willbe apparent from the description and drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIGS. 1 a-1 c show views of an enrollment device.

FIG. 2 shows and illustration of the connections and control of thevarious components of an enrollment device.

FIG. 3 shows exemplary specifications for an enrollment device.

FIGS. 4 a-4 e are photographs of a working example of an enrollmentdevice.

FIGS. 5, 6 a and 6 b show views of alternate embodiments of anenrollment device.

FIG. 7 is a flow diagram illustrating operation of an enrollment device.

FIG. 8 is a diagram of an exemplary processor.

FIG. 9 is an illustration of a system featuring an enrollment device.

FIG. 10 illustrates modules included in an enrollment device.

FIG. 11 illustrates image processing by an enrollment device.

FIG. 12 illustrates a Hough transform.

FIG. 13 illustrates segmented address information.

FIGS. 14, 14 a-14 b, 15 a-15 c, and 16 illustrate graphical userinterface screens for an enrollment device.

DETAILED DESCRIPTION

FIGS. 1 a, 1 b, and 1 c illustrate an exemplary embodiment of anenrollment device 100. Referring to the cutaway view of FIG. 1 a, thedevice body 102 (also referred to herein as “main enclosure”) includes atransparent tempered glass surface 104 for receiving a package 106(shown in FIGS. 1 b and 1 c). Load cells 108 (e.g. solid state loadcells) are located at the corners of the glass surface and provideweight information for items placed on the surface 104.

The device body 102 includes two cameras 110. First and second surfacemirrors 112 are disposed to direct an image of a package placed on thesurface to the cameras. The marginal rays of the camera/mirror systemsare indicated in FIG. 1 a. As shown, the combined field of view of thetwo cameras 110 substantially covers the area of the glass surface 104,allowing image capture of package 106 placed at an arbitrary position onthe glass surface 104.

The device body also includes a computer processor 114 which may becoupled to the various components of the device and/or to externalsystems or devices. For example, the computer processor 114 may be anx86 platform capable of running Linux or embedded Microsoft Windowsproducts. In various embodiments, this computer may run the internal“firmware” for the device as well as support application facilities suchas a Web Server and postal rating (i.e. pricing/metering) engine.

In some embodiments, the device body 102 includes one or more lightingmodules (not shown), such as light emitting diode modules, to illuminatethe package placed on the glass surface.

A support arm 116 (also referred to herein as an “extension arm”)extends above the surface 104. The support arm 116 includes controlbuttons 118 (e.g. power control, measurement units control, scale tare,etc.). A display 120 provides information to the user or users (e.g.postal clerk and/or customer) and may include for example, a characterdisplay (e.g. LCD display). The support arm 116 also includes anultrasonic transducer rangefinder 122 which operates to capture one ormore dimensions of package 106 placed on the glass surface 104 (e.g. theheight dimension as shown in FIGS. 1 b and 1 c). In some embodiments,the device 100 may include additional or alternative rangefinders (e.g.infrared rangefinder, mechanical rangefinder, laser rangefinder, radarrange finder, LED based rangefinder, one or more cameras, etc.)

FIG. 2 illustrates the connections and control of the various componentsof an enrollment device of the type described above. Compact personalcomputer (PC) 202 (e.g. comprising processor 114) is connected to amicrocontroller 204. The microcontroller receives analog inputs fromfour load cells 206 and an infrared rangefinder 208, along with digitalinputs from an ultrasonic rangefinder 210 and user control buttons 212.Information from these inputs can be passed back to the compact PC 202for processing. The microcontroller 204 also provides digital controloutputs to a display 214, LED indicators 216, and a beeper 218. Thecompact PC 202 receives image information from each of two cameras 220for processing (e.g. image processing, OCR, dimension capture, etc). Thecompact PC 202 is further connected to various peripherals 221 via aconnection such as a universal serial bus (USB) hub 222. The peripheralsmay include a printer, an RFID reader capable of receiving signals froman RFID tag on the package, and various displays and controllers (e.g.keyboard, touch screen display, touchpad, etc.).

As will be understood by one skilled in the art, FIG. 3 lists variousparameters and specifications for a working example of an enrollmentdevice of the type described above, along with target performancespecifications corresponding to typical applications. Note that themajority of performance characteristics of the working example are ingeneral compliance with target values.

FIGS. 4 a-4 e are photographs of a working example of an enrollmentdevice of the type described above. FIG. 4 a shows the device with apackage placed on the glass surface. FIG. 4 b shows the device alongwith display and control peripherals. FIG. 4 c shows a compact PCintegrated into the main enclosure. FIGS. 4 d and 4 e show examples ofimage processing, dimension capture, and OCR, as will be discussed ingreater detail below.

Although an exemplary embodiment is presented above, it is to beunderstood that other suitable configurations for the enrollment devicemay be used. For example, FIG. 5 shows a perspective view of anexemplary embodiment of an enrollment device 100. In this configuration,instruments such as an ultrasonic rangefinder 122 and/or RFID reader areincorporated in a spherical enclosure 502 on top of an extension armpositioned at the corner of the device's main enclosure 102. Controlbuttons 118 and an organic LED (OLED) display 120 are positioned on themain enclosure 102.

FIG. 6 a shows another exemplary embodiment, in which cameras 110 areplaced on the extension arm 116 instead of in a main enclosure of thedevice, thereby providing a top down view of a package placed on thesurface 104 of a weight scale 601. In some applications, thisconfiguration may provide additional comfort for users accustomed toplacing packages with labels or other printed information “face up”,while still allowing for dimension capture, OCR, etc. As shown,processor 114 is located externally, but in other embodiments it may belocated integrally.

FIG. 6 b shows a similar embodiment featuring a single camera 110.Camera 110 may have a field of view larger than and encompassing surface104, such that even packages which are as large or larger than packagereceiving surface 104 of weight scale 601 may be imaged. Camera 110 mayinclude an autofocus or other focusing and/or alignment systems. Indicia602 on surface 104 may be used to aid in focusing and/or alignment ofcamera 110.

FIG. 7 illustrates the flow of an enrollment process 700 using a device100 of the type described above. Initially, in step 701 the package tobe enrolled is received on the receiving surface 104 of the enrollmentdevice 100. In step 702, the presence of the package is detected, forexample, as described in greater detail below, by processing a stream ofvideo images captured by the cameras (or camera) 110.

Once the presence of the package is detected, multiple types ofinformation about the package are captured in parallel steps. In step702, the weight of the object is captured, e.g. by the load cells 198 orscale 601.

In step 702, the cameras 110 capture one or more images of the package.The images undergo a processing step 703 to provide information aboutthe package. For example, in step 705 machine vision applications (e.g.edge detection) may be used to capture one or more dimensions (e.g.length, width) of the package. Optical character recognition techniquescan be used in step 704 to capture text or other markings on the package(e.g., postal markings/permits, bar codes, etc.).

In step 706, one or more dimensions of the package are captured. Forexample, the height of the package may be determined by the ultrasonicrange finder 122. This information can be combined with dimensioninformation determined in the image processing step to provide completedimensional information (e.g. length, width and height) of the package.

In step 707, the enrollment device 100 captures other types ofinformation related to the package. For example, an RFID readerconnected to or integrated with the enrollment device can gatherinformation from an RFID tag on the package.

In step 708, the information captured in the above described steps isthen collected, processed, and/or stored. The information may also beoutput, for example to a delivery service business system. Theinformation may be output in any suitable form including electronicdata, an analog signal, printed material, visual display, etc.

For example, in some embodiments, information is displayed to a user viaa graphical user interface. The user may confirm or edit the capturedinformation, enter additional information, query a customer as to achoice of delivery options or additional services, etc. In someembodiments, printed material (e.g. labels, stamps, etc.) may be outputfrom an attached or integral printer. In some embodiments, output caninclude markings (e.g. barcodes) printed directly onto the packageusing, for example, an attached or integral spray printing system, orthrough attaching separately printed labels with bar code, postage, orrelated package information—based on information derived from thedevice.

In some embodiments, the performance of one or more steps might dependon the results of other steps. For example, the imaging and OCR of apackage might determine that the package was a “flat rate” envelope ofthe type common in postal and delivery services. In such a case, weightand dimensional information is not relevant, and thus the steps used tocapture this type of information may be omitted.

FIG. 8 a shows an exemplary embodiment of processor 114. Video signalsfrom cameras 110 are input to frame stitching module 801 which combinesmultiple overlapping views of surface 104 into a single view (inembodiments featuring a single camera may omit this module). Thecombined video signal is passed to dimension capture module 802 andrecognition module 803. Rangefinder signal may also be passed fromrangefinder 122 to dimension capture module 802 and recognition module803. Using, e.g. the techniques described herein, dimension capturemodule 802 operates to produce dimension data indicative of the size(e.g. length, width, and/or height) of a package based on the inputsignals. For example, module 802 may determine the length and width ofthe object based on edge finding processing of the combined video signaland the height of the package based on the rangefinder signal.

Using, e.g. the techniques described herein, recognition module 804operates to produce character data related to one or more characters(e.g. alphanumeric address, bar code, postal mark, symbols, etc) foundon the package.

Weight module 804 receives a weight signal input from a weight sensorsuch as load cells 122 or scale 601, and produces weight data indicativeof the weight of a package placed on surface 104.

Processor 114 combines the weight, dimension, and character data frommodules 802, 803, and 804 and outputs the data from output 805. Theoperation of the modules described above will be further describedbelow.

FIG. 9 illustrates the integration of an enrollment device 100 into anexemplary delivery system 900. As described above, an enrollment device100 (captures numerous pieces of information which are passed on to andprocessed by processor 114 (e.g. via firmware run by a compact PCintegrated with or linked to device 100). Processor 114 may communicate(e.g. using a network connection), with one or more servers 901. Forexample, an address management server could exchange information relatedto redirection or alternate delivery. A rights management server couldexchange information to validate permits or confirm postage. Asupervised delivery server could exchange information related to packagetracking or chain of custody (e.g. for prescription medications or legalevidence). In some embodiments, these servers might further interactwith other “back end” applications including supervised deliveryapplication 902 and database management applications 903. Suchapplications could be connected via a network 904 (e.g., an intranet,extranet, the world wide web, etc.)

Processor 114 interacts with a point of service (POS) system 905 (e.g. apostal service counter sales system) to provide, for example, validatedaddress or redirection information, weight, dimensions, etc.Interactions might be mediated by an event handler application 906 whichinterrupts or otherwise communicates with the POS system to provide, forexample, invalid permit, address, or delivery point warnings,redirection information, scale/OCR timeout indications, etc.

Enrollment Functions

The following describes more detailed examples of the various functionswhich may be carried out by enrollment device 100.

Scale Function

In some embodiments, the enrollment device 100 includes a scale 601 foracquiring information about the weight of a package. For example, invarious embodiments, a solid state weighing device (e.g. including oneor more load cells 118) operates with accuracies consistent withrelevant standards (e.g. US Postal Service and/or Royal Mailrequirements). Direct management of a display device may be provided insupport of weights and measure requirement.

In some embodiments, detailed usage history is kept in order to ensureaccurate performance throughout the life of the scale. Remotesupervision may be provided (e.g. via an internet connection providedthrough an integrated compact PC). Suspect scales can be identified viaan analytics application.

Imaging Function

In typical applications, the enrollment device 100 detects the presenceof a package and captures an image of at least a portion of the package.The image is processed to derive information from the package (e.g. frommailing labels or printed markings) including: printedaddress/destination info, sending identification information, postalmarkings, and other information such as proprietary barcode information.In various embodiments the enrollment device acquires this informationin an automated fashion, performed in such a way as to have reducednegative impact on currently sorting.

Referring to FIG. 10, in some embodiments, the image related tasks ofthe enrollment device are performed by four modules: the imaging devicemodule 1001, the tracking module, the image enhancement and dimensioncapture module 1003 and the recognition module 1004. All or portions ofthe above modules may be included in processor 114.

The imaging device module 1001 employs one or more cameras 110 to obtainimages of a package. The imaging device module 1001 may operate to meettwo different sets of requirements imposed by the tracking module 1002and the recognition module 1004. As will be described below, mail piecetracking module 1002 typically requires image capture with a relativelylarge field of view and a relatively high frame rate, but can toleraterelatively low resolution. The recognition module 1004, on the otherhand, requires relatively high resolution images (e.g. about 200 dotsper inch, “dpi”), but can typically tolerate a relatively narrow fieldof view and relatively slower frame rate. Accordingly, in someembodiments, the imaging device module 1001 operates in a first mode toprovide a low resolution but large field of view (e.g. substantiallycovering the surface 104 of a device 100) and high frame rate imagestream to the tracking module 1002. When a package is placed onreceiving surface 104 of the enrollment device 100, the tracking moduleidentifies the package's presence, location (i.e. position and/ororientation), and size. The imaging module 1001, using information fromthe tracking module 1002, then switches to a high resolution mode tocapture high quality images of areas of interest (e.g. an area includingan address label) on the package.

Note that in various embodiments these modules may be implemented inhardware (e.g. using multiple cameras or sensors of varying resolution)or in software (e.g. using image processing techniques known in the art)or in a combination thereof.

As mentioned above, the tracking module 1002 operates to monitor astream of image information from the imaging device module 1001 todetect the presence of and determine the size and location/orientationof a package placed on receiving surface 104 of the enrollment device100. Several tracking techniques will be described herein, however, itis to be understood that the tracking function may be performed by anysuitable techniques (e.g. using known machine vision applications).

In some embodiments, the tracking module 1002 employs a color maskingmodule 1005. Color masking is a technique used when looking for anobject which leverages unique color information that the object mighthave (e.g., brow coloring for parcels) and/or that the background mayhave (e.g. the known color of surface 104). In typical applications, thecolor masking process consists of removing any pixel of an image thatdeviates to a specific range of color values.

For this type of approach, the well known RGB color space is sometimesnot the most appropriate if one wants to avoid artifacts due to lightinginconsistencies. Instead, computing color deviations in the YUV or theYCbCr color spaces typically leads to better results. For reference, Yis usually referred to a luminance and turning an RGB color value in theYCbCr color space can be done through these simple relationships:

Y=0.31R+0.59G+0.11B;Cr=R−Y;Cb=B−Y

The advantage of this color representation is that lightinginconsistencies will typically incur radial shifts of the (Cb, Cr) valuearound the center of this plane. Hence the angle of a polarrepresentation of this color plane can be fairly invariant throughlighting changes. It is also noteworthy to notice that this angle isclosely related to the concept of a color's hue.

In some embodiments, the tracking module 1002 employs motion analysisusing, for example, frame differencing module 1006. For example, one wayto detect motion is through a frame differencing process. As the system(e.g. featuring a stationary camera) gathers successive video frames itsimply compares each pixel value to its value in the previous frames andremoves those that have not changed significantly. When the images areprovided as grayscale, intensity is the only available parameter but inthe case of color images there are alternative ways to perform thesedifferences depending on the color space.

Such a frame differencing process is effectively a temporal high-passfilter and as such it is highly prone to pixel noise. Therefore it isoften coupled with subsequent image processing stages such as linear ormorphologic filters, which are discussed below.

FIG. 11 shows an example of frame difference tracking. A short series ofvideo frames 1101 were captured of an envelope being handled in a“visually busy” environment. These frames were further imported withinthe Matlab environment where the differences between successive frameswere computed. These difference images 1102, illustrated in the secondrow of FIG. 10 b, reveal the mail piece. However, the frame differencingalso reveals any other moving object, such as the person's hand and arm.

In order to identify a rectangular object (e.g. a package or envelope)in the frame differences, in some embodiments, the tracking module 1002employs the Hough transform module 1007 to transform the framedifferenced data 1102 to produce Hough domain images 1103. The primarypurpose of this transform is to extract linear graphic elements (i.e.straight lines) from an image. It effectively does so by maintaining aseries of accumulators that keep track of all lines that pass through aset of points. As many of these points are collinear, the largest ofthese accumulators reveal the equation of that line in the Hough domain.In that domain, the y-axis corresponds to the orientation of that lineand the x-axis corresponds to the distance between that line and anorigin one chooses in the image. This mapping is shown in FIG. 12. Forexample, FIG. 12 shows three points in the spatial domain. For each oneof these points, all the lines that pass through it are represented by a“vertical sinusoid” in the Hough domain. Because these three pointswhere chosen to be collinear, notice that the three correspondingsinusoids intersect. The coordinates (θ, ρ) of this intersectiondescribe the line that passes through all three points uniquely.

Referring back to FIG. 11, the third row of Hough domain images 1103shows the Hough domain that corresponds to each frame difference 1102.As the motion of the mail piece slows down (i.e. third column in theFIG. 11) and the difference frame starts to show a clear rectangularoutline of the mail piece.

Note, as shown in the inset of FIG. 11, that the Hough domain sharpensup, revealing two noticeable peaks lined up horizontally. The fact thatthese peaks live on the same horizon in the Hough domain reveals thatthese two corresponding lines are parallel: one has thus found the upperand lower edges of the mail piece.

If one were to further look for linear feature that are perpendicular tothese edges one would simply look for local maximums in the Hough domainat the horizon corresponding to a 90 degrees rotation. In the case ofthe current example this would further reveal an estimation of the leftand right edges of the mail piece.

Rectangle tracking module 1008 can leverage information of the typedescribed above to track the location/orientation of rectangularpackages. Frame differencing and a Hough transform provide a solid basisfor the tracking of a moving rectangular object. It has the greatbenefits of further providing orientation estimation for the mail piecein the same process, while requiring no further assumption concerningthe size or even the aspect ratio of the rectangular object.

In typical applications, color masking and motion analysis can reveal“blobs” (connected regions) of pixels that maybe of interest. In somecases this might be not enough to locate the target or an area ofinterest. As previously noted, shape-related image analysis techniquessuch as the Hough transformation can provide additional information.Some techniques useful for tracking include, for example blobsegmentation clustering. One useful step is to group pixels that maybelong to the same spatial blob. These techniques are discussed furtherin the context of image enhancement and OCR below.

One way to quantify a blob of pixels is by measuring its spatialmoments. The first order moment is simply the blob's center of mass. Itssecond order moments provide measures about how “spread” the blob isaround its center of mass. Through a simple diagonalization processthese second order moments can further lead to the blob's principalcomponents, which provide a general measure of the object's aspect ratioand its orientation. In a 1962 publication, Ming-Kuei Hu suggested ameans to normalize and combine the second and third central moments of agraphical object, leading to a set of 7 descriptors that have since beenreferred to as the Hu-moments. These 7 features have the highlydesirable properties of being translation, rotation and scale invariant.A number of OCR engines have subsequently been developed based on thesefeatures.

Extracting the edges of a visual object is also a very common step thatmay come handy as one searches for a target mail piece. One of the mostpopular methods is the Canny edge detection algorithm. It is equivalentto the location of local maximums in the output of a high frequency(gradient) filter. The method actually starts with the application of alow-pass filter in order to reduce noise in the image so the wholeprocess can be seen as some band-pass filtering stage followed by amorphologic processing stage.

Once a package presence has been detected and location, orientation, andsize determined by the tracking module 1002, one or more images of thepackage at a desired resolution are obtained by the imaging devicemodule and passed on to the image enhancement module 1003. In variousembodiments, this module operates to process these images to compensatefor the amount of rotation from ideal registration (i.e. registrationwith the edges of the surface 104 of the enrollment device 100) that wasdetected by the mail piece tracking module. As is known in the art, thiscan be achieved through, for example, a resampling stage. In typicalapplications, this resampling stage does not require any more than abilinear interpolation between pixels.

As required by the application or environment at hand, some embodimentsemploy other image enhancement processing techniques to provide a highquality image to the recognition module 1004 for, for example, accurateOCR.

Depending on the OCR performance achieved, a further segmentation module1009 may be added to the image enhancements module. The typical imageanalysis technique will make a certain number of assumptions concerningthe input image. Some of these assumptions might be reasonable in thecontext of the application and some others might require a little bit ofwork on the input. This is where preprocessing typically comes intoplay. As a general rule, the object of a preprocessing stage is toemphasize or reveal salient features of an image while dampingirrelevant or undesirable ones before attempting to perform furtheranalysis of the image's content. There are numerous types of processingknown in the art that may share such an objective. Some such processingtypes are composed of elementary stages that fall within one of thefollowing major categories: color manipulations, linear filters,morphological image processing, or image segmentation.

Color manipulations include grayscale conversion from a color image,color depth reduction, thresholding (to a binary image for instance),brightness and contrast modifications, color clipping, negation and manyothers. In such processes, the color value of an output pixel is adirect function of the input color value of that same pixel and someglobal parameters. In some cases, these global parameters might bederived from an overall analysis of the input image but once chosen theyremain the same during the processing of all pixels in the image.

Linear image filters can typically be seen as a convolution between theinput image and another (usually smaller) image that's sometime referredto as a kernel. Their objective is to reveal certain spatial frequencycomponents of the image while damping others. The most commonly usedlinear filters are either blurring (low-pass) or sharpening (high-pass)the image. Gradients and differentiators used for edge detection areanother commonly used type of high-pass linear filters. Performing abrute force 2D convolution can be a computationally expensiveproposition. Indeed if the filter kernel M is a square image counting Nrows and N columns, processing a single input pixel through the kernelwill require N² operations. One way to overcome this prohibitive scalingis to use what are sometimes referred to as separable filters. Those arefilters for which the kernel M is an outer-product of two vectors: i.e.M=UV^(T), where U and V are vectors of length N.

With such a choice for the filter, the sliding correlation with thematrix M over the entire image can be expressed as the cascade of two 1Dfiltering stages over the two dimensions (horizontal and vertical) ofthe image. The elements of the vector V are the impulse response of the1D filtering stage we first apply to each row and the elements of thevector U are the impulse response of the 1D filtering stage wesubsequently apply to each column. Each 1 D filtering stage involves Noperations per pixel and therefore, the entire sliding correlation withthe matrix M involves only 2N operations (as opposed to N² if the filterwere not separable).

The most common separable filters are Gaussian low-pass filters. Theseparability of their kernel falls out from the fact that the product oftwo Gaussians is also a Gaussian. Note that the same technique can beapplied for separable kernels that are not square (i.e. the vectors Uand V have different lengths). In cases where the kernel in notseparable, one may use techniques known in the art to approximate thekernel as a combination of separable filtering stages. These techniqueswill typically perform an eigenvalue decomposition of the kernel.

Other noteworthy special cases of separable linear filters are filtersfor which the kernel matrix is filled with the same value. These areeffectively low pass filters that average all pixel values over arectangular neighborhood centered on the pixel position. Although theymight exhibit less than ideal frequency responses they have the greatadvantage of being computationally cheap. Indeed regardless of thekernel size, their computation consists of simple running sums performedsubsequently over the horizontal and vertical direction of the image,requiring a total of only 4 operations per pixel.

Morphological image processing is a type of processing in which thespatial form or structure of objects within an image are modified.Dilation (objects grow uniformly), erosion (objects shrink uniformly)and skeletonization (objects are reduced to “stick figures”) are threefundamental morphological operations. Typically, these operations areperformed over binary images for which there is a clear concept ofpresence and absence of an object at every pixel position but theseconcepts have also been extended to grayscale images.

Binary image morphological operations are based on the concept ofconnectivity between pixels of the same class. From an implementationpoint of view, these operations typically consist of a few iterationsthrough a set of hit or miss transformations. A hit or misstransformation is effectively a binary pattern lookup table. While alinear filter would apply a fixed linear combination of the input inorder to set the output value of a pixel, this process will set a pixelto either 1 or 0 depending on whether its surrounding pattern is foundin the table or not (Hence the terms “hit or miss”). Depending on thelookup table, this can effectively implement a highly non-linearoperation.

Image segmentation includes the division of an image into regions (orblobs) of similar attributes. As discussed below, an OCR system willtypically include at least one image segmentation stage. In fact, manysuitable image analysis algorithms aiming to localize, identify orrecognize graphical elements perform some form of image segmentation.

In general terms this process may consists of a clustering orclassification of pixel positions based on a local graphical measure.This graphical measure is the image attribute that should be fairlyuniform over a region. In other words, the resulting regions or blobsshould be homogeneous with respect to some local image characteristic.This local measure may simply consist of the pixel's color but someapplications may require more sophisticated measures of the image'slocal texture around that pixel position. It is also generallyunderstood that a segmentation process should aim to reveal regions orblobs that exhibit rather simple interiors without too many small holes.

The nature of the chosen graphical attribute depends entirely on theapplication and the type of blobs one is trying to isolate. For example,segmenting an image into text versus non-text regions will require somesort of texture attribute while segmenting light versus dark areas willonly require color intensity as an attribute.

Once the chosen attribute has been computed throughout the image, theremainder of the segmentation process will typically use an ad-hocalgorithm. One of the most intuitive techniques is sometimes referred toa region growing and its recursive nature is very similar in spirit to afloodfill algorithm. More sophisticated techniques implement clusteringprocesses using classical iterative algorithms known in the art such ask-means or ISODATA.

In some applications, it may be necessary to increase the resolution ofthe captured image or images. In some embodiments, resolution of theimage may be increased using a technique know as superresolution. TheNyquist sampling criterion requires that the sampling frequency shouldbe at least double for the highest frequency of the signal or imagefeatures one wishes to resolve. For a given image module 1001 focallength, this typically implies that the smallest optical feature one canresolve will never be smaller then 2 pixels-worth of a pixilatedsensor's (e.g. CCD's) resolution.

A common practice to overcome this theoretical limit is to combinemultiple captures of the same object from slightly differentperspectives. While each capture suffers from Nyquist's limit they form,together, a non-uniform but higher frequency sampling of the object. Thekey to this process is the ability to align these multiple captures withsub-sample accuracy. Once the individual captures are up-sampled andaligned, they can carefully averaged based on their sampling phase. Thisprocess effectively re-constructs a capture of the object with highersampling frequency, and hence a higher image resolution. Variations ofsuch techniques are known from, for example, the field of imageprocessing.

Once an image has been processed by the image enhancement module 1003,it is passed on to the recognition module 1004. The recognition moduleoperates to derive information from, for example, labels or printedmarkings on the object using e.g., OCR. While it is to be understoodthat any suitable OCR technique or tool may be used, in the followingseveral exemplary OCR techniques will be described.

Various embodiments provide the ability to isolate text within aprovided image and to turn it reliably into text, e.g., ASCII codes. Agoal of OCR is to recognize machine printed text using, e.g., a singlefont of a single size or even multi-font text having a range ofcharacter sizes.

Some OCR techniques exploit the regularity of spatial patterns.Techniques like template matching use the shape of single-fontcharacters to locate them in textual images. Other techniques do notrely solely on the spatial patterns but instead characterize thestructure of characters based on the strokes used to generate them.Despite the considerable variety in the techniques employed, manysuitable OCR systems share a similar set of processing stages.

One OCR stage may include extraction of the character regions from animage: This stage will typically use ancillary information known inorder to select image properties that are sufficiently different for thetext regions and the background regions as the basis for distinguishingone from the other. One common technique when the background is a knownsolid color (white for instance) is to apply iterative dichotomies basedon color histograms. Other techniques might make use of known charactersizes or other spatial arrangements.

Another OCR stage may include segmentation of the image into text andbackground. Once provided with image regions that contain text the goalof this stage is to identify image pixels that belong to text and thosethat belong to the background. The most common technique used here is athreshold applied to the grayscale image. The threshold value may befixed using ancillary knowledge about the application or by usingmeasures calculated in the neighborhood of each pixel to determine anadaptive local threshold.

Another OCR stage may include conditioning of the image: The imagesegments resulting from segmentation may contain some pixels identifiedas belonging to the wrong group. This stage consists of a variety oftechniques used to clean it up and delete noise.

Yet anther OCR stage may include segmentation of characters: Sometechniques will subsequently segment the input image into regions thatcontain individual characters but other algorithms will avoid this stageand proceed with character recognition without prior charactersegmentation. This latter technique is driven by the realization that inmany cases character segmentation turns out to be a more difficultproblem than recognition itself.

Some OCR stages include normalization of character size: Once the imageis segmented into characters, one may adjust the size of the characterregions so that the following stages can assume a standard charactersize. Systems that rely on size-independent topological features fortheir character recognition stages might not require such normalization.

OCR systems typically include feature detection: Many different featuredetection techniques are known in the art. Some template matching isused to find the whole character as a feature, while other systems seeksub features of the characters. These may include boundary outlines, thecharacter skeleton or medial axis, the Fourier or Wavelet coefficientsof the spatial pattern, various spatial moments and topologicalproperties such as the number of holes in a pattern.

A classification stage may be used to assign, to a character region, thecharacter whose properties best match the properties stored in thefeature vector of the region. Some systems use structural classifiersconsisting of a set of tests and heuristics based on the designer'sunderstanding of character formation. Other classifiers take astatistical rather than structural approach, relying on a set oftraining samples and using statistical techniques to build a classifier.These approaches include the Bayes decision rule, nearest neighborlookups, decision trees, and neural networks.

In a verification stage knowledge about the expected result is used tocheck if the recognized text is consistent with the expected text. Suchverification may include confirming that the extracted words are foundin a dictionary, or otherwise match some external source of information(e.g. if city information and zip code information in a U.S. postaladdress match). This stage is obviously application dependent.

In various embodiments, the recognition module 1004 may employ any ofthe above described techniques, alone or in combination.

Recognition of handwritten characters (sometimes referred to as ICR)may, in some applications, be more challenging. In the context ofapplications such as tablet computers or PDA, the ICR engine will oftentake advantage of pen stroke dynamics. Of course this type ofinformation is not available from the optical capture of a hand-writtendocument. Such applications may require the system to be restricted to asmaller number of permissible characters (e.g. upper caps or numeral)and/or rely heavily on a small lexicon

For example, when text is handwritten in cursive it is often difficultto segment each letter separately so rather than operating as an opticalcharacter recognition, an ICR system will often operate as a “Wordrecognizer”, looking to the best match between the graphical object anda small lexicon of recognizable words. In order to achieve asatisfactory recognition rate, this lexicon might need to be as small as10 words or so.

In various embodiments, the performance of an OCR system may beincreased by specializing to the task at hand by restricting its lexiconor dictionary so that it can effectively recover from few characterrecognition errors the same way a computer (e.g. running a wordprocessor) might be able to correct a typo.

Maintaining a restricted and dynamic lexicon is more effective when adocument has a rigid and known structure. Without such structure itmight not be possible to use a lexicon any smaller than a dictionary forthe language at hand.

Fortunately, as shown in FIG. 13 an address appearing on a mail piece istypically a relatively highly structured a document. This is why theUSPS can OCR a large part of the machinable mail pieces even whenaddress are hand-written.

In typical embodiments, a proper usage of OCR should take into accountsome typical shortcomings. Generality must be considered versusaccuracy. A single classifier might be trained to get improved resultsin limited circumstances (a single font for instance) but itsperformance will typically drop when the size of its training setincreases. Consequently, modern classifiers are in fact conglomerates ofclassifiers coupled with a mechanism to consolidate their results. Thisin turn will tend to further increase the already substantialcomputational requirements of the system if it is intended to cope witha large variety of fonts.

Non uniform backgrounds may present challenges. OCR algorithms typicallytake advantage of the fact that the text is presented on a uniformbackground that has sufficiently high contrast between text andbackground colors. When the background is not uniform, OCR recognitionrates are substantially decreased. In those cases and in order to removea non-uniform background from the image, additional preprocessing stagesmight be required prior to the various ones we've presented above.

Image resolution should be considered. OCR technologies were developedwithin the context of scanned physical documents. Although opticalscanning might lead to various artifacts such as noise and slightskewing, these will also typically operate at higher image resolutions(<200 dpi). As discussed above, imaging module 1001 may provide imagesat such resolutions, e.g. by employing digital cameras known in the art.

Most mail pieces will already convey some machine-readable data (e.g.bar codes, postal marks) by the time it reaches an enrollment device. Invarious embodiments, the enrollment device may read these markings usingOCR, or using additional sensors (e.g. a barcode reader).

FIG. 4 d shows the output display of an exemplary embodiment of anenrollment device 100. The display shows the captured image of a packageplaced on the device, along with information acquired from labels andmarkings on the package using the OCR techniques described above. Thisembodiment was able to accommodate OCR of packages placed at anarbitrary angle on receiving surface 104, using, for example, therotation correction techniques described above.

Information obtained using OCR is passed on for, for example, addressquality, meter enforcement, value added service subsystems, and operatorinput. In some embodiments, the OCR facility will be able to readdocuments such as passports, driver licenses, credit cards, coupons,tickets, etc. Simply placing the document anywhere on the receivingsurface 104 will trigger a read and document analysis. Form capture isalso supported with the ability to allow customers to, for example,present completed forms for immediate OCR results available to thepostal clerk. Certain forms such as customs declarations can be handledmuch more efficiently with this facility.

Dimension Capture Function

In typical applications, accurately determining the dimensions of apackage at enrollment may be crucial for determining, for example, therate of postage. For example, postal rates may depend on an objectslength, width, height, and/or combinations thereof.

As noted above, during image acquisition and processing, one or moredimensions of a package placed on an enrollment device may bedetermined. For example, FIG. 4 e shows an output display of anexemplary embodiment of an enrollment device 100. The display shows thecaptured image 401 of a package, a difference image 402, and a Houghplane image 403 generated using the techniques described above. Asindicated in the captured image 401, the system has successfullyidentified the edges of the face of the object imaged by the device.This allows the device to calculate and output the length and width ofthe package.

The height dimension is captured using, for example, ultrasonic rangefinder 122, thereby providing complete dimensional information. Anultrasonic transducer emits sound waves and receives sound wavesreflected by objects in its environment. The received signals areprocessed to provide information about the spatial location of theobjects. For example, in the embodiment shown in FIGS. 1 a-1 c, therangefinder can determine the vertical position of the top surface ofthe package 106 relative to the receiving surface 104. One advantage ofultrasonic rangefinder over optical rangefinders is that it is able tounambiguously detect optically transparent surfaces (e.g. the glasssurface 104 of FIGS. 1 a-1 c).

It is to be understood that, in various embodiments, other suitabledimension capture techniques may be used. Some embodiments may employother types of rangefinders (e.g. optical sensors). In some embodiments,the top (or other) surface of a package may be located mechanically bybringing a sliding arm or a user held wand in contact with the surfacepackage, and detecting the position of the arm or wand. In someembodiments, more than two dimensions of the package may be determinedbased on captured image data, for example, by stereoscopically imagingthe object from multiple perspectives.

Although the examples above generally include dimension capture ofrectangular objects, it is to be understood that the techniquesdescribed above can be extended to objects of any arbitrary shape.

RFID Function

If an item has an RFID tag it will be detected and read by an RFIDperipheral attached to or integrated with the enrollment device 100. Theacquired data is then available for further processing and/or output todownstream applications.

Processing and User Interface Functions

As discussed above, the enrollment device may process the myriad ofcaptured data related to a package and output relevant information to auser. In some embodiments, information is displayed to a user through aninteractive graphical user interface (GUI). For example, as shown inFIG. 14, the user may navigate back and forth through a series ofscreens 1401 a, 1401 b, and 1401 c using, for example, a mouse,keyboard, or touch screen device. Referring to FIG. 14 a, screen 1401 ashows an image of the package along with captured data. The user mayconfirm the captured information and/or choose to proceed to screen 1401b, shown in detail in FIG. 14 b, for editing the captured data and/oradding additional data. Once all relevant information about the packagehas been captured and confirmed or otherwise entered, a further screen1401 c presents various delivery service options.

In some embodiments an expert system employing “backward chaining” logicmay be employed to receive and analyze the wealth of information comingfrom the enrolment device. As is known in the art, in typicalapplications, backward chaining starts with a list of goals (or ahypothesis) and works backwards from the consequent to the antecedent tosee if there is data available that will support any of theseconsequents. An inference engine using backward chaining would searchthe inference rules until it finds one which has a consequent (Thenclause) that matches a desired goal. If the antecedent (If clause) ofthat rule is not known to be true, then it is added to the list of goals(in order for your goal to be confirmed you must also provide data thatconfirms this new rule).

The system can use such techniques to generate multiple service optionsbased on the captured information and/or user requirements. As shown inFIGS. 15 a, 15 b, and 15 c, these options may be organized and presented(e.g. to a customer or salesperson) in a convenient fashion using, forexample, a touch screen interface.

FIG. 16 shows another example of a sequence of GUI screens.

In some embodiments, USB and Ethernet connections will be provided. Someembodiments will include additional USB, keyboard, and displayconnections. In some embodiments the firmware/software will supportSimple Object Access Protocol/Service Oriented Architecture Protocol(SOAP) calls. Some embodiments will support a Web Server, rating engine,and/or maintenance facilities.

In some embodiments, an embedded computing platform, e.g. processor 114,contained in or peripheral to the enrolment device 100 allows it tooperate as a stand alone postage meter. In some embodiments, theenrolment device 100 brings an intelligent item assessment capability tothe corporate mail room. Shippers can be assured that the services theyrequire will be correctly calculated and that items shipped will be infull compliance with the terms of service. Additionally, in someembodiments, the enrolment device will be able to communicate directlywith the post office allowing billing directly from SAP, sales andmarketing support, and convenient automatic scheduling of pick ups.Rates and incentives can be system wide, applied to a subset ofcustomers, or even be specific to an individual customer.

Display and Control Functions

In some embodiments, the main on-device control function is presented bythree OLED captioned buttons. The captions are dynamic and are managedby the firmware. An application programming interface (API) allows(possibly external) applications to control the buttons when thefirmware is not using them. Operational, maintenance, and diagnosticfunctions are supported. If required, the extension arm can have adisplay attached, for example, if required by local regulation.

Additional Features

Various embodiments include one or more of the following features:

-   -   The embedded computing platform will have a PSD (Postal Security        Device) built in.    -   The enrolment device will use available information (e.g. from        an intranet or internet connection) to establish its location.    -   The embedded electronics will have a secure “black box” data        recorder for audit and control purposes. This capability can be        remotely accessed (e.g. via an intranet or internet connection).    -   Cryptographic capabilities consistent with export regulations        will be available.    -   User management and tokens will be supported. Departmental        accounting is possible. SAP codes will be supported.    -   Work Flow Systems Integration and support for manufacturing        systems will be available.    -   Dashboard facilities with remote access will be supported.    -   Automatic integration with dispatch centers will be supported.    -   Embedded wireless broadband will be available.    -   Ability to read full size checks and bill payment forms.    -   Ability to capture signed documents for onward processing or        item truncation.    -   Extensive device support including but not limited to;        -   Card Readers,        -   Printers,        -   Postal Label Printers,        -   Interactive Customer Display,        -   Pin Input devices,        -   Keyboards.

Exemplary Applications

Based on the ability to provide the general business benefits describedabove, the devices techniques described have commercial applications inthe following market segments:

Managed Content

The evolution of the letter stream as a facility to carry packetssuitable for delivery to unattended delivery points requires theaddition of infrastructure allowing that activity. The enrolment of suchitems requires the ability to capture all of the information requiredfor all items to be sent through the mail stream as well as new valueadded services. The ability to tag items with services such as coldstream, work flow notifications, risk management, call centernotifications, and conditional deliveries will be required. In someembodiments, the enrollment device would be located in locations such aspharmacies or dedicated shipping centers where prescriptions were beingprepared for shipment to patients.

Postal Operators & Courier Companies:

For the highly automated postal operators and courier companies, theenrollment device provides automated “front end” data collection,leveraging their existing investment in systems and technology.

For the low or non-automated strata of postal operators and couriercompanies, the enrollment device provides a low-cost automation solutionfor the capture and management of shipment related information at theircounter locations, eliminating a range of paper-based processes andenabling integration with 3rd party carriers and systems.

The Pharmaceutical Industry:

The enrollment device provides the pharmaceutical industry with a meansof automating the Provenance and Chain of Custody aspects of theirbusiness.

Civil Defense:

The enrollment device provides a mechanism for the mass distribution ofproducts and services with a clear Chain of Custody from point ofInduction.

Goods Distribution Companies:

It is anticipated that Goods Distribution companies will benefit fromthe ability to use the enrollment device to manage and prepare their“one-to many” shipments.

One or more or any part thereof of the techniques described above can beimplemented in computer hardware or software, or a combination of both.The methods can be implemented in computer programs using standardprogramming techniques following the examples described herein. Programcode is applied to input data to perform the functions described hereinand generate output information. The output information is applied toone or more output devices such as a display monitor. Each program maybe implemented in a high level procedural or object oriented programminglanguage to communicate with a computer system. However, the programscan be implemented in assembly or machine language, if desired. In anycase, the language can be a compiled or interpreted language. Moreover,the program can run on dedicated integrated circuits preprogrammed forthat purpose.

Each such computer program is preferably stored on a storage medium ordevice (e.g., ROM or magnetic diskette) readable by a general or specialpurpose programmable computer, for configuring and operating thecomputer when the storage media or device is read by the computer toperform the procedures described herein. The computer program can alsoreside in cache or main memory during program execution. The analysismethod can also be implemented as a computer-readable storage medium,configured with a computer program, where the storage medium soconfigured causes a computer to operate in a specific and predefinedmanner to perform the functions described herein.

A number of embodiments of the invention have been described.Nevertheless, it will be understood that various modifications may bemade without departing from the spirit and scope of the invention.

As used herein the terms “light” and “optical” and related terms are tobe understood to include electromagnetic radiation both within andoutside of the visible spectrum, including, for example, ultraviolet andinfrared radiation.

The examples above refer to a package received by the enrollment device.It is to be understood that suitable item may be received and enrolled,including: mail pieces, pharmaceutical items, evidentiary items,documents, containers of any type, etc.

A number of references have been incorporated in the currentapplication. In the event that the definition or meaning of anytechnical term found in the references conflicts with that found herein,it is to be understood that the meaning or definition from the instantapplication holds.

1-40. (canceled)
 41. Non-transitory computer readable media storingcomputer executable instructions, which when executed by a processor,cause the processor to carry out a method comprising: receiving a videosignal of an image of a package on a receiving surface, the video signalincluding a stream of two dimensional image frames; generating from thereceived video signal tracking data indicative of a presence and alocation of the package on the receiving surface, wherein the trackinginformation comprises length and width dimension data indicative of thesize of the package along two axes lying in a plane parallel to thereceiving surface; and producing dimension data indicative of a size ofthe package based on the tracking data.
 42. The non-transitory computerreadable medium of claim 41, wherein the method further comprisesgenerating weight data indicative of the weight of the package.
 43. Thenon-transitory computer readable medium of claim 41, wherein the methodfurther comprises generating character data indicative of one or morecharacters present on the package.
 44. The non-transitory computerreadable medium of claim 41, wherein the tracking data further comprisesheight dimension data indicative of the size of the package along anaxis transverse to the plane parallel to the receiving surface.
 45. Thenon-transitory computer readable medium of claim 41, wherein the methodfurther comprises generating height dimension data in response toreceiving a stereoscopic video signal.
 46. The non-transitory computerreadable medium of claim 41, wherein the method further comprisesgenerating edge data indicative of the location of one or more edges ofthe package.
 47. The non-transitory computer readable medium of claim41, wherein the method further comprises: generating two dimensionaldifference images from two or more two dimensional images of the videosignal; and generating information indicative of the presence orlocation of the package based on color information from the videosignal.
 48. The non-transitory computer readable medium of claim 41,wherein the method further comprises: applying a Hough transformation toone or more images from the video signal; and analyzing the transformedimages to determine information indicative of the size of location ofedges on the package.
 49. The non-transitory computer readable medium ofclaim 41, wherein the method further comprise: processing one or moretwo dimensional images from the video signal; and analyzing the one ormore processed images to produce character data indicative of one ormore characters present on the package.
 50. The non-transitory computerreadable medium of claim 41, wherein the method further comprises:modifying the color of the one or more images; applying a linear imagefilter to the one or more images; and applying one or more morphologicaloperation to the one or more images.
 51. The non-transitory computerreadable medium of claim 41, wherein the method further comprisessegmenting the one or more images into one or more regions of interestbased on the content of the images.
 52. The non-transitory computerreadable medium of claim 41, wherein the method further comprisesrotating at least a portion of the one or more images.
 53. Thenon-transitory computer readable medium of claim 41, wherein the methodfurther comprises combining overlapping two dimensional images from atleast two cameras to produce a single image of a combined field of viewof the cameras.
 54. The non-transitory computer readable medium of claim41, wherein the processor is communicatively coupled to at least aweight sensor and a video camera unit.
 55. The non-transitory computerreadable medium of claim 54, wherein the weight sensor generates aweight signal indicative of a weight of the package.
 56. Thenon-transitory computer readable medium of claim 55, wherein the weightsensor comprises at least one of: a load cell, a MEMs device, apiezoelectric device, a spring scale, and a balance scale.
 57. Thenon-transitory computer readable medium of claim 54, wherein the videocamera unit generates the video signal indicative of the image of thepackage on the receiving surface, wherein the video signal comprises astream of two dimensional image frames.
 58. The non-transitory computerreadable medium of claim 57, wherein the video camera unit comprises astereoscopic imager.
 59. The non-transitory computer readable medium ofclaim 57, wherein the video camera unit comprises at least two cameras,each camera unit generating a respective video signal, and wherein thecameras have at least partially overlapping fields of view.
 60. Thenon-transitory computer readable medium of claim 57, wherein the videocamera unit comprises an infrared camera.